Deep Learning for Natural Language Inference

Samuel Bowman, Xiaodan Zhu


Abstract
This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting-edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning models for language understanding and reasoning.
Anthology ID:
N19-5002
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Anoop Sarkar, Michael Strube
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6–8
Language:
URL:
https://aclanthology.org/N19-5002
DOI:
10.18653/v1/N19-5002
Bibkey:
Cite (ACL):
Samuel Bowman and Xiaodan Zhu. 2019. Deep Learning for Natural Language Inference. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pages 6–8, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Deep Learning for Natural Language Inference (Bowman & Zhu, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-5002.pdf
Presentation:
 N19-5002.Presentation.pdf
Video:
 https://aclanthology.org/N19-5002.mp4
Data
MultiNLI